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Aerts, Hugo

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Aerts

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Hugo

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Aerts, Hugo
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    Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma
    (Public Library of Science, 2015) Grove, Olya; Berglund, Anders E.; Schabath, Matthew B.; Aerts, Hugo; Dekker, Andre; Wang, Hua; Velazquez, Emmanuel Rios; Lambin, Philippe; Gu, Yuhua; Balagurunathan, Yoganand; Eikman, Edward; Gatenby, Robert A.; Eschrich, Steven; Gillies, Robert J.
    Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.
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    Defining the biological basis of radiomic phenotypes in lung cancer
    (eLife Sciences Publications, Ltd, 2017) Grossmann, Patrick; Stringfield, Olya; El-Hachem, Nehme; Bui, Marilyn M; Rios Velazquez, Emmanuel; Parmar, Chintan; Leijenaar, Ralph TH; Haibe-Kains, Benjamin; Lambin, Philippe; Gillies, Robert J; Aerts, Hugo
    Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images. DOI: http://dx.doi.org/10.7554/eLife.23421.001
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    Revisiting inconsistency in large pharmacogenomic studies
    (F1000Research, 2017) Safikhani, Zhaleh; Smirnov, Petr; Freeman, Mark; El-Hachem, Nehme; She, Adrian; Rene, Quevedo; Goldenberg, Anna; Birkbak, Nicolai J.; Hatzis, Christos; Shi, Leming; Beck, Andrew; Aerts, Hugo; Quackenbush, John; Haibe-Kains, Benjamin
    In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis — that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.
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    Phylogenetic ctDNA analysis depicts early stage lung cancer evolution
    (2017) Abbosh, Christopher; Birkbak, Nicolai J.; Wilson, Gareth A.; Jamal-Hanjani, Mariam; Constantin, Tudor; Salari, Raheleh; Le Quesne, John; Moore, David A; Veeriah, Selvaraju; Rosenthal, Rachel; Marafioti, Teresa; Kirkizlar, Eser; Watkins, Thomas B K; McGranahan, Nicholas; Ward, Sophia; Martinson, Luke; Riley, Joan; Fraioli, Francesco; Al Bakir, Maise; Grönroos, Eva; Zambrana, Francisco; Endozo, Raymondo; Bi, Wenya; Fennessy, Fiona; Sponer, Nicole; Johnson, Diana; Laycock, Joanne; Shafi, Seema; Czyzewska-Khan, Justyna; Rowan, Andrew; Chambers, Tim; Matthews, Nik; Turajlic, Samra; Hiley, Crispin; Lee, Siow Ming; Forster, Martin D.; Ahmad, Tanya; Falzon, Mary; Borg, Elaine; Lawrence, David; Hayward, Martin; Kolvekar, Shyam; Panagiotopoulos, Nikolaos; Janes, Sam M; Thakrar, Ricky; Ahmed, Asia; Blackhall, Fiona; Summers, Yvonne; Hafez, Dina; Naik, Ashwini; Ganguly, Apratim; Kareht, Stephanie; Shah, Rajesh; Joseph, Leena; Quinn, Anne Marie; Crosbie, Phil; Naidu, Babu; Middleton, Gary; Langman, Gerald; Trotter, Simon; Nicolson, Marianne; Remmen, Hardy; Kerr, Keith; Chetty, Mahendran; Gomersall, Lesley; Fennell, Dean; Nakas, Apostolos; Rathinam, Sridhar; Anand, Girija; Khan, Sajid; Russell, Peter; Ezhil, Veni; Ismail, Babikir; Irvin-sellers, Melanie; Prakash, Vineet; Lester, Jason; Kornaszewska, Malgorzata; Attanoos, Richard; Adams, Haydn; Davies, Helen; Oukrif, Dahmane; Akarca, Ayse U; Hartley, John A; Lowe, Helen L; Lock, Sara; Iles, Natasha; Bell, Harriet; Ngai, Yenting; Elgar, Greg; Szallasi, Zoltan; Schwarz, Roland F; Herrero, Javier; Stewart, Aengus; Quezada, Sergio A; Peggs, Karl S.; Van Loo, Peter; Dive, Caroline; Lin, Jimmy; Rabinowitz, Matthew; Aerts, Hugo; Hackshaw, Allan; Shaw, Jacqui A; Zimmermann, Bernhard G.; Swanton, Charles
    Summary The early detection of relapse following primary surgery for non-small cell lung cancer and the characterization of emerging subclones seeding metastatic sites might offer new therapeutic approaches to limit tumor recurrence. The potential to non-invasively track tumor evolutionary dynamics in ctDNA of early-stage lung cancer is not established. Here we conduct a tumour-specific phylogenetic approach to ctDNA profiling in the first 100 TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study participants, including one patient co-recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and perform tumor volume limit of detection analyses. Through blinded profiling of post-operative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients destined to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastases, providing a new approach for ctDNA driven therapeutic studies
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    Author Correction: Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR
    (Nature Publishing Group UK, 2018) Trebeschi, Stefano; van Griethuysen, Joost J. M.; Lambregts, Doenja M. J.; Lahaye, Max J.; Parmar, Chintan; Bakers, Frans C. H.; Peters, Nicky H. G. M.; Beets-Tan, Regina G. H.; Aerts, Hugo
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    Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma
    (2017) Dunn, William D.; Aerts, Hugo; Cooper, Lee A.; Holder, Chad A.; Hwang, Scott N.; Jaffe, Carle C.; Brat, Daniel J.; Jain, Rajan; Flanders, Adam E.; Zinn, Pascal O.; Colen, Rivka R.; Gutman, David A.
    Background: Radiological assessments of biologically relevant regions in glioblastoma have been associated with genotypic characteristics, implying a potential role in personalized medicine. Here, we assess the reproducibility and association with survival of two volumetric segmentation platforms and explore how methodology could impact subsequent interpretation and analysis. Methods: Post-contrast T1- and T2-weighted FLAIR MR images of 67 TCGA patients were segmented into five distinct compartments (necrosis, contrast-enhancement, FLAIR, post contrast abnormal, and total abnormal tumor volumes) by two quantitative image segmentation platforms - 3D Slicer and a method based on Velocity AI and FSL. We investigated the internal consistency of each platform by correlation statistics, association with survival, and concordance with consensus neuroradiologist ratings using ordinal logistic regression. Results: We found high correlations between the two platforms for FLAIR, post contrast abnormal, and total abnormal tumor volumes (spearman’s r(67) = 0.952, 0.959, and 0.969 respectively). Only modest agreement was observed for necrosis and contrast-enhancement volumes (r(67) = 0.693 and 0.773 respectively), likely arising from differences in manual and automated segmentation methods of these regions by 3D Slicer and Velocity AI/FSL, respectively. Survival analysis based on AUC revealed significant predictive power of both platforms for the following volumes: contrast-enhancement, post contrast abnormal, and total abnormal tumor volumes. Finally, ordinal logistic regression demonstrated correspondence to manual ratings for several features. Conclusion: Tumor volume measurements from both volumetric platforms produced highly concordant and reproducible estimates across platforms for general features. As automated or semi-automated volumetric measurements replace manual linear or area measurements, it will become increasingly important to keep in mind that measurement differences between segmentation platforms for more detailed features could influence downstream survival or radio genomic analyses.
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    Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
    (Nature Publishing Group UK, 2017) Vallières, Martin; Kay-Rivest, Emily; Perrin, Léo Jean; Liem, Xavier; Furstoss, Christophe; Aerts, Hugo; Khaouam, Nader; Nguyen-Tan, Phuc Felix; Wang, Chang-Shu; Sultanem, Khalil; Seuntjens, Jan; El Naqa, Issam
    Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.
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    Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
    (Nature Pub. Group, 2014) Aerts, Hugo; Velazquez, Emmanuel Rios; Leijenaar, Ralph T. H.; Parmar, Chintan; Grossmann, Patrick; Cavalho, Sara; Bussink, Johan; Monshouwer, René; Haibe-Kains, Benjamin; Rietveld, Derek; Hoebers, Frank; Rietbergen, Michelle M.; Leemans, C. René; Dekker, Andre; Quackenbush, John; Gillies, Robert J.; Lambin, Philippe
    Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.
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    Importance of collection in gene set enrichment analysis of drug response in cancer cell lines
    (Nature Publishing Group, 2014) Bateman, Alain R.; El-Hachem, Nehme; Beck, Andrew; Aerts, Hugo; Haibe-Kains, Benjamin
    Gene set enrichment analysis (GSEA) associates gene sets and phenotypes, its use is predicated on the choice of a pre-defined collection of sets. The defacto standard implementation of GSEA provides seven collections yet there are no guidelines for the choice of collections and the impact of such choice, if any, is unknown. Here we compare each of the standard gene set collections in the context of a large dataset of drug response in human cancer cell lines. We define and test a new collection based on gene co-expression in cancer cell lines to compare the performance of the standard collections to an externally derived cell line based collection. The results show that GSEA findings vary significantly depending on the collection chosen for analysis. Henceforth, collections should be carefully selected and reported in studies that leverage GSEA.
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    Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
    (Public Library of Science, 2014) Parmar, Chintan; Rios Velazquez, Emmanuel; Leijenaar, Ralph; Jermoumi, Mohammed; Carvalho, Sara; Mak, Raymond; Mitra, Sushmita; Shankar, B. Uma; Kikinis, Ron; Haibe-Kains, Benjamin; Lambin, Philippe; Aerts, Hugo
    Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.